Identification.
While several prior works measure the extent to which the model is relying on “context” in a general sense (e.g., the model is relying on something other than the tennis racket to detect the tennis racket) [1]}, [2]}, [3]}, SPIRE identifies specific SPs (e.g., the model is relying on the person to detect the tennis racket).
For identifying specific SPs, the most common approach uses explainable machine learning [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
For image datasets, these methods rely on local explanations, resulting in a slow process that requires the user to look at the explanation for each image, infer what that explanation is telling them, and then aggregate those inferences to assess whether or not they represent a consistent pattern (Figure REF ).
In addition to this procedural difficulty, there is uncertainty about the usefulness of some of these explanations for model debugging [14]}, [15]}.
